Image segmentation refers to the process to divide an image into meaningful non-
overlapping regions according to human perception, which has become a classic topic since …

Road extraction from high-resolution remote sensing images is a challenging but hot
research topic in the past decades. A large number of methods are invented to deal with this …

Superpixels group perceptually similar pixels to create visually meaningful entities while
heavily reducing the number of primitives for subsequent processing steps. As of these …

We present an improved version of the Simple Linear Iterative Clustering (SLIC) superpixel
segmentation. Unlike SLIC, our algorithm is non-iterative, enforces connectivity from the …

Superpixels provide an efficient low/mid-level representation of image data, which greatly
reduces the number of image primitives for subsequent vision tasks. Existing superpixel …

We present in this paper a superpixel segmentation algorithm called Linear Spectral
Clustering (LSC), which produces compact and uniform superpixels with low computational …

Computer vision applications have come to rely increasingly on superpixels in recent years,
but it is not always clear what constitutes a good superpixel algorithm. In an effort to …

In this paper, we propose a real-time image superpixel segmentation method with 50
frames/s by using the density-based spatial clustering of applications with noise (DBSCAN) …

This paper proposes a methodology to estimate the transmission in underwater
environments which consists on an adaptation of the Dark Channel Prior (DCP), a statistical …

Generic object level saliency detection is important for many vision tasks. Previous
approaches are mostly built on the prior that “appearance contrast between objects and …